{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tips & Trics for anypytools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stopping and restarting simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AnyPyProcess caches the simulation results. This allows us to stop the simulations, and then later restart them again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anypytools import AnyPyProcess \n",
    "app = AnyPyProcess(num_processes=1)\n",
    "macro = [\n",
    "    'load \"Knee.any\"', \n",
    "    'operation Main.MyStudy.InverseDynamics',\n",
    "    'run',\n",
    "]\n",
    "\n",
    "macrolist = [macro]*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bcaed4b92354e10b64c128bec9df392",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=20.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt: User aborted\n",
      "Completed: 18, Not processed: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "app.start_macro(macrolist); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we stopped the simulation using the Notebook interrupt button. Calling the `start_macro()` function again continues the processing and re-run any task that wasn't completed in the first run and any task that exited with errors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6281e4173b6c41818949831580473382",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=20.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Completed: 20\n"
     ]
    }
   ],
   "source": [
    "app.start_macro(macrolist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: That changing the input arguments `start_macro` or re-instanciating the `app` object will erase the cache and re-run all processes. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Including meta-information in the output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `start_macro()` also returns extra meta info, but the information is not printed by the default `__repr__()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5743868ee0654b668bb3a612688c4ce0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Completed: 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Main.MyStudy.Output.MaxMuscleActivity': \n",
       "   array([0.00890538, 0.02510015, 0.06036529, 0.08096677, 0.08356285,\n",
       "          0.08356285, 0.08096678, 0.06036529, 0.02510015, 0.00890538]),"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess\n",
    "from anypytools.macro_commands import Load, OperationRun, Dump\n",
    "\n",
    "app = AnyPyProcess()\n",
    "macro = [\n",
    "    Load('Knee.any', defs={'N_STEP':10}), \n",
    "    OperationRun('Main.MyStudy.InverseDynamics'),\n",
    "    Dump('Main.MyStudy.Output.MaxMuscleActivity'),\n",
    "]\n",
    "result = app.start_macro(macro)[0]\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But the information is there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['load \"Knee.any\" -def N_STEP=\"10\"',\n",
       " 'operation Main.MyStudy.InverseDynamics\\nrun',\n",
       " 'classoperation Main.MyStudy.Output.MaxMuscleActivity \"Dump\"',\n",
       " 'exit']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[\"task_macro\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see all task information by evaluating the result object as standard Python dictionary: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Main.MyStudy.Output.MaxMuscleActivity': array([0.00890538, 0.02510015, 0.06036529, 0.08096677, 0.08356285,\n",
       "        0.08356285, 0.08096678, 0.06036529, 0.02510015, 0.00890538]),\n",
       " 'task_macro_hash': '5310ca3fa2025ef0',\n",
       " 'task_id': 0,\n",
       " 'task_work_dir': 'C:\\\\Users\\\\mel\\\\Documents\\\\AnyBodySource\\\\AnyPyTools\\\\docs\\\\Tutorial',\n",
       " 'task_name': 'docs-Tutorial-0',\n",
       " 'task_processtime': 0.7587850093841553,\n",
       " 'task_macro': ['load \"Knee.any\" -def N_STEP=\"10\"',\n",
       "  'operation Main.MyStudy.InverseDynamics\\nrun',\n",
       "  'classoperation Main.MyStudy.Output.MaxMuscleActivity \"Dump\"',\n",
       "  'exit'],\n",
       " 'task_logfile': ''}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving output to re-process at a later time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The extra task meta info gives other posibilities. The results from running batch processing (i.e. output f `start_macro()` can be used as input to restart the same processing even if the AnyPyProcess have no cached results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a54db5f31b145cb891ec91237245dc0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Completed: 1\n"
     ]
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess \n",
    "\n",
    "app = AnyPyProcess()\n",
    "macro = [\n",
    "    Load('Knee.any', defs={'N_STEP':10}), \n",
    "    OperationRun('Main.MyStudy.InverseDynamics'),\n",
    "    Dump('Main.MyStudy.Output.MaxMuscleActivity'),\n",
    "]\n",
    "output = app.start_macro(macro) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c6b0259f7304c5da300b9920c6d3308",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Completed: 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'Main.MyStudy.Output.MaxMuscleActivity': \n",
       "    array([0.00890538, 0.02510015, 0.06036529, 0.08096677, 0.08356285,\n",
       "           0.08356285, 0.08096678, 0.06036529, 0.02510015, 0.00890538])]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app = AnyPyProcess()\n",
    "app.start_macro(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The effect is that the result of an analysis can be saved to files and later restarted.  The next example illustrates this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Saving data to disk while running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from scipy.stats import distributions\n",
    "from anypytools import AnyPyProcess, AnyMacro\n",
    "from anypytools.macro_commands import Load, SetValue_random, OperationRun, Dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data saved\n",
      "Data saved\n",
      "Data saved\n",
      "Data saved\n",
      "Data saved\n",
      "Data saved\n",
      "Data saved\n",
      "Data saved\n",
      "Data saved\n",
      "Data saved\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "\n",
    "tibia_knee_srel = distributions.norm([0, 0.18, 0], [0.005, 0.005, 0.005] ) \n",
    "femur_knee_srel = distributions.norm([0, -0.3, 0], [0.005, 0.005, 0.005] ) \n",
    "\n",
    "app = AnyPyProcess(silent=True)\n",
    "mg = AnyMacro(number_of_macros = 500)\n",
    "mg.extend([\n",
    "    Load('knee.any', defs = {'N_STEP':20}),\n",
    "    SetValue_random('Main.MyModel.Tibia.Knee.sRel', tibia_knee_srel),\n",
    "    SetValue_random('Main.MyModel.Femur.Knee.sRel', femur_knee_srel),\n",
    "    OperationRun('Main.MyStudy.InverseDynamics'),\n",
    "    Dump('Main.MyStudy.Output.MaxMuscleActivity'),\n",
    "])\n",
    "\n",
    "try:\n",
    "    os.remove('data.db')\n",
    "except OSError:\n",
    "    pass\n",
    "\n",
    "for macros in mg.create_macros_MonteCarlo(batch_size=50):\n",
    "    app.start_macro(macros)\n",
    "    app.save_results('data.db', append=True)\n",
    "    print('Data saved')\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All this stored data can be be reloaded "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entries in file: 500\n"
     ]
    }
   ],
   "source": [
    "reloaded_results = app.load_results('data.db')\n",
    "print('Entries in file: {}'.format(len(reloaded_results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Main.MyStudy.Output.MaxMuscleActivity': \n",
       "    array([0.0145094 , 0.01838543, 0.02723008, 0.03983322, 0.05388536,\n",
       "           0.06596098, 0.07287488, 0.07568929, 0.07757901, 0.07764066,\n",
       "           0.07764066, 0.07757901, 0.07568929, 0.07287487, 0.06596096,\n",
       "           0.05388536, 0.03983322, 0.02723023, 0.01838528, 0.0145094 ])]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reloaded_results[456:457]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(reloaded_results['MaxMuscleAct'].T, 'b', lw=0.2, alpha = 0.3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "widgets": {
   "state": {
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